add float unit_test for conv2d
Signed-off-by: jing.deng <Jing.Deng@verisilicon.com>
This commit is contained in:
Jing.Deng
Kainan Cha
parent
be0a566042
commit
3f6d697cb8
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@ -0,0 +1,942 @@
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#include "tim/vx/ops/conv2d.h"
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#include "tim/transform/layout_inference.h"
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "gtest/gtest.h"
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TEST(Conv2d, shape_4_2_1_1_float32_PaddingTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({4, 2, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 1, 3}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{4, 2, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {
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1, 1, 1, 1, // row = 1
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2, 2, 3, 2 // row = 2
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};
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// weight data oihw
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std::vector<float> weight_data = {
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1, 2, 3, 4, //first 2x2 filter
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-1, 1, -1, 1, // second 2x2 filter
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-1, -1, 1, 1, // third 2x2 filter
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};
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// bias data
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std::vector<float> bias_data = {1, 2, 3};
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// nchw
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std::vector<float> golden = {// first channel
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18, 22, 21, 8, 7, 9, 8, 3, 2, 3, 1, -1,
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// second channel
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2, 3, 1, 0, 5, 6, 6, 4, -1, -2, -2, 1};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_4_2_2_2_float32_PointwiseTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({4, 2, 2, 2}); //whcn
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tim::vx::ShapeType weight_shape({1, 1, 2, 1}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{4, 2, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {
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0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1,
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0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2
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};
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// weight data oihw
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std::vector<float> weight_data = {
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1, 2 // first filter
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};
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// bias data
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std::vector<float> bias_data = {0};
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// nchw
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std::vector<float> golden = {
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1.5, 1.5, 1.5, 1.5, 3, 3, 3, 3,
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1.5, 3, 4.5, 6, 1.5, 3, 4.5, 6
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};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_4_2_1_2_float32_SimpleTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({4, 2, 1, 2}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 1, 3}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 1, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {
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// First batch
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1, 1, 1, 1, // row = 1
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2, 2, 2, 2, // row = 2
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// Second batch
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1, 2, 3, 4, // row = 1
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1, 2, 3, 4, // row = 2
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};
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// weight data oihw
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std::vector<float> weight_data = {
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1, 2, 3, 4, -1, 1, -1, 1, -1, -1, 1, 1
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};
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// bias data
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std::vector<float> bias_data = {1, 2, 3};
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// nchw
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std::vector<float> golden = {18, 18, 2, 2, 5, 5, 17, 37, 4, 4, 3, 3};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_4_2_2_2_float32_SimpleChannelsTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({4, 2, 2, 2}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 2, 3}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 1, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data
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std::vector<float> input_data = {
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0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1,
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0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2};
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// weight data
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std::vector<float> weight_data = {1, 2, 3, 4, 1, 2, 3, 4, -1, 1, -1, 1,
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-1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1};
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// bias data
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std::vector<float> bias_data = {1, 2, 3};
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std::vector<float> golden = {18, 18, 2, 2, 5, 5, 17, 37, 4, 4, 3, 3};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_6_3_1_1_float32_SimpleAnisotropicStridesTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({6, 3, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 1, 1}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 2, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {
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3, 2, 1, -1, -2, -3, 4, 3, 2, -2, -3, -4, 5, 4, 3, -3, -4, -5
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};
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// weight data oihw
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std::vector<float> weight_data = {
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1, 2, //
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3, 4, //
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};
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// bias data
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std::vector<float> bias_data = {-1};
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// nchw
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std::vector<float> golden = {
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30, -24, //
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40, -34, //
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};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({3, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::VALID;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<float> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({4, 3, 1, 1}); //whcn
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tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{4, 3, weight_shape[3], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
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};
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// weight data oihw
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std::vector<float> weight_data = {
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1, 4, 7, 2, 5, 8, 3, 6, 9
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};
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// bias data
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std::vector<float> bias_data = {0};
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// nchw
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std::vector<float> golden = {
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105, 150, 183, 95, 235, 312,
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357, 178, 187, 234, 261, 121
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};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedConstFilterTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 3, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{4, 3, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 4, 7, 2, 5, 8, 3, 6, 9
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {0};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
105, 150, 183, 95, 235, 312,
|
||||
357, 178, 187, 234, 261, 121
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::SAME;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedBiasTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 3, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{4, 3, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 4, 7, 2, 5, 8, 3, 6, 9
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {10};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
115, 160, 193, 105, 245, 322, 367, 188, 197, 244, 271, 131
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::SAME;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedValidTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 3, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{2, 1, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 4, 7, 2, 5, 8, 3, 6, 9
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {0};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
312, 357
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_2_2_2_float32_DisabledPointwiseMultifilterTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 2, 2, 2}); //whcn
|
||||
tim::vx::ShapeType weight_shape({1, 1, 2, 2}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{4, 2, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1,
|
||||
0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 2, 2, 3
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {0};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
1.5, 1.5, 1.5, 1.5, 3, 3, 3, 3, 2.5, 2.5, 2.5, 2.5, 5, 5, 5, 5,
|
||||
1.5, 3, 4.5, 6, 1.5, 3, 4.5, 6, 2.5, 5, 7.5, 10, 2.5, 5, 7.5, 10
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_9_9_1_1_float32_SimpleDilationTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({9, 9, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{3, 3, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 2, 3, 4, 5, 6, 7, 8, 9
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {0};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {5, 5, 5, 5, 5, 5, 5, 5, 5};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({3, 3});
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_2_1_2_float32_StrideTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 2, 1, 2}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 1, 3}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{3, 1, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
1, 1, 1, 1, 2, 2, 3, 2, 1, 2, 3, 4, 1, 2, 4, 4
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 2, 3, 4, -1, 1, -1, 1, -1, -1, 1, 1
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {1, 2, 3};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
18, 22, 21, 2, 3, 1, 5, 6, 6, 17, 31, 40, 4, 5, 3, 3, 4, 4
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Conv2d, shape_4_2_1_2_float32_InputAndFilterSameWidthHeightTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({4, 2, 1, 2}); //whcn
|
||||
tim::vx::ShapeType weight_shape({4, 2, 1, 1}); //whio
|
||||
tim::vx::ShapeType bias_shape({weight_shape[3]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{1, 1, weight_shape[3], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {
|
||||
1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 3, 4, 1, 2, 3, 4
|
||||
};
|
||||
|
||||
// weight data oihw
|
||||
std::vector<float> weight_data = {
|
||||
1, 2, 3, 4, -1, -1, 1, 1
|
||||
};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {0};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {
|
||||
10, 34
|
||||
};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({0, 0});
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
weight_shape[3], padding, ksize, stride, dilation);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
Loading…
Reference in New Issue